from earm.lopez_embedded import model from max_plus_consumption_production import run_tropical import numpy as np import os import helper_functions as hf directory = os.path.dirname(__file__) parameters_path = os.path.join(directory, "parameters_5000") all_parameters = hf.listdir_fullpath(parameters_path) parameters = hf.read_pars(all_parameters[0]) t = np.linspace(0, 20000, 100) run_tropical(model, t, parameters, diff_par=1, type_sign='consumption', sp_visualize=[6])
axHistx.hist(column(cparp_info, 1), bins=np.arange(min(solver.tspan), max(solver.tspan) + binwidthx, binwidthx), weights=weightsx) # axHistx.axis["bottom"].major_ticklabels.set_visible(False) for tl in axHistx.get_xticklabels(): tl.set_visible(False) axHistx.set_yticks([0, 0.5, 1]) # axHisty.axis["left"].major_ticklabels.set_visible(False) for tl in axHisty.get_yticklabels(): tl.set_visible(False) axHisty.set_xticks([0, 0.5, 1]) axApop.legend(loc=0) fig.savefig('/home/oscar/Documents/tropical_project/all_parameters_earm.png', format='png', dpi=400) return all_parameters_path = hf.listdir_fullpath('/home/oscar/tropical_project_new/parameters_5000') clusters_path = hf.listdir_fullpath('/home/oscar/tropical_project_new/parameters_clusters') cluster_pars_path = {} for sc in clusters_path: ff = open(sc) data_paths = csv.reader(ff) params_path = [dd[0] for dd in data_paths] cluster_pars_path[sc.split('clusters/')[1]] = params_path display_observables(all_parameters_path) def display_all_species(cluster_parameters): """Saves figures of all species for each cluster of parameters
print (tropical_data) drivers_all = [set(dr.keys()) for dr in tropical_data] drivers_over_pars = set.intersection(*drivers_all) drivers_to_df = {} for sp in drivers_over_pars: tmp = [0] * len(drivers_all) for idx, tro in enumerate(tropical_data): tmp[idx] = tro[sp] drivers_to_df[sp] = tmp for sp in drivers_to_df.keys(): pandas.DataFrame(np.array(drivers_to_df[sp]), index=rindex, columns=cindex).to_csv(path + '/data_frame%d' % sp + '.csv') return from earm.lopez_embedded import model t = np.linspace(0, 20000, 100) pars = hf.listdir_fullpath('/home/oscar/home/oscar/Documents/tropical_project/parameters_5000') compare_all_drivers_signatures(model, t, pars[:10], to_data_frame=True, dir_path='/home/oscar/Desktop') # all_drivers = np.load('/home/oscar/Documents/tropical_projetct/drivers_all_parameters5000.npy') # drivers_all = {idx: dr.keys() for idx, dr in enumerate(all_drivers)} # # for i in drivers_all: # for j in drivers_all: # if set(drivers_all[i]) == set(drivers_all[j]): # if i != j: # print (i, j)
from miscellaneous_analysis import parameter_distribution from earm.lopez_embedded import model import helper_functions as hf # Script to get the comparison of parameter distribution between different parameter clusters in EARM clus = hf.listdir_fullpath('/home/oscar/Documents/tropical_earm/clustered_parameters_bid') new_path = '/home/oscar/Documents/tropical_earm/parameters_5000' for par in model.parameters: parameter_distribution(clus, par.name, new_path)